Abstract
The paper studies Ant Colony Communities (ACC). They are used to solve the Dynamic Travelling Salesman Problem (DTSP). An ACC consists of a server and a number of client ACO colonies. The server coordinates the work of individual clients and sends them cargos with data to process and then receives and integrates partial results. Each client implements the basic version of the ACO algorithm. They communicate via sockets and therefore can run on several separate computers. In the DTSP distances between the nodes change constantly. The process is controlled by a graph generator. In order to study the performance of the ACC, we conducted a substantial number of experiments. Their results indicate that to handle highly dynamic distance matrixes we need a large number of clients.
References
Held, M., Karp, R.M.: A dynamic programming approach to sequencing problems. J. Soc. Ind. Appl. Math. 10(1), 196–210 (1962)
Applegate, D.L., Bixby, R.E., Chvatal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton (2011)
Antosiewicz, M., Koloch, G., Kamińskim, B.: Choice of best possible metaheuristic algorithm for the Travelling Salesman Problem with limited computational time: quality, uncertainty and speed. J. Theor. Appl. Comput. Sci. 7(1), 46–55 (2013)
Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italie (1992)
Psarafits, H.N.: Dynamic vehicle routing: status and prospects. Nat. Tech. Annal. Oper. Res. 61, 143–164 (1995)
Guntsch, M., Middendorf, M.: Pheromone modifcation strategies for ant algorithms applied to dynamic TSP. In: EvoWorkshops 2001: Applications of Evolutionary Computation, pp. 213–222 (2001)
Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Proceeding of 2nd European Workshop on Evolutionary Computation in Combinatorial Optimization (EvoCOP-2002), vol. 2279, pp. 72–81 (2002)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes in dynamic environments. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 371–380. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15871-1_38
Dorigo, M., Stuetzle, T.: Ant Colony Optimization: overview and recent advances. IRIDIA - Technical Report Series, Technical Report No. TR/IRIDIA/2009-013, May 2009
Siemiński, A.: TSP/ACO Partameter Optimization; Information Systems Architecture and Technology; System Analysis Approach to the Design, Control and Decision Support; pp. 151–161. Oficyna Wydawnicza Politechniki Wrocławskiej Wrocław (2011)
Gaertner, D., Clark, K.L.: On optimal parameters for Ant Colony Optimization algorithms. In: IC-AI, pp. 83–89 (2005)
Pedemonte, M., Nesmachnow, S., Cancela, H.: A survey on parallel Ant Colony Optimization. Appl. Soft Comput. 11, 5181–5197 (2011)
Siemiński, A., Kopel, M.: Comparing efficiency of ACO parallel implementations. J. Intell. Fuzzy Syst. 32(2), 1377–1388 (2017)
Chirico, U.: A Java framework for ant colony systems. In: Ants2004: Forth International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels (2004)
Siemiński, A.: Measuring efficiency of Ant Colony Communities. In: Zgrzywa, A., Choroś, K., Siemiński, Aj (eds.) Multimedia and Network Information Systems. AISC, vol. 506, pp. 203–213. Springer, Cham (2017). doi:10.1007/978-3-319-43982-2_18
Hong, T.-P., Peng, Y.-C., Lin, W.-Y., Wang, S.-L.: Empirical comparison of level-wise hierarchical multi-population genetic algorithm. J. Inf. Telecommun. 1(1), 66–78 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Siemiński, A. (2017). Solving Dynamic Traveling Salesman Problem with Ant Colony Communities. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-67074-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67073-7
Online ISBN: 978-3-319-67074-4
eBook Packages: Computer ScienceComputer Science (R0)